In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Play around with view_sentence_range to view different parts of the data.
view_sentence_range = (0, 10)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))
sentences = source_text.split('\n')
word_counts = [len(sentence.split()) for sentence in sentences]
print('Number of sentences: {}'.format(len(sentences)))
print('Average number of words in a sentence: {}'.format(np.average(word_counts)))
print()
print('English sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
print()
print('French sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of each sentence from target_text. This will help the neural network predict when the sentence should end.
You can get the <EOS> word id by doing:
target_vocab_to_int['<EOS>']
You can get other word ids using source_vocab_to_int and target_vocab_to_int.
def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):
"""
Convert source and target text to proper word ids
:param source_text: String that contains all the source text.
:param target_text: String that contains all the target text.
:param source_vocab_to_int: Dictionary to go from the source words to an id
:param target_vocab_to_int: Dictionary to go from the target words to an id
:return: A tuple of lists (source_id_text, target_id_text)
"""
# TODO: Implement Function
source_id_text= source_text.split('\n')
for i in range(len(source_id_text)):
source_id_text[i]=[source_vocab_to_int[word] for word in source_id_text[i].split()]
#print(source_id_text)
target_id_text=target_text.split('\n')
for i in range(len(target_id_text)):
target_id_text[i]=target_id_text[i]+' <EOS>'
target_id_text[i]=[target_vocab_to_int[word] for word in target_id_text[i].split()]
print(source_id_text,'\n', target_id_text)
return (source_id_text, target_id_text)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_text_to_ids(text_to_ids)
Running the code cell below will preprocess all the data and save it to file.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
helper.preprocess_and_save_data(source_path, target_path, text_to_ids)